期刊論文
| 學年 | 102 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2014-07-01 |
| 作品名稱 | A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients |
| 作品名稱（其他語言） | |
| 著者 | K.-J. Wang; B. Makond; K.-H. Chen |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Applied Soft Computing 20, pp.15-24 |
| 摘要 | In this study, we propose a set of new algorithms to enhance the effectiveness of classification for 5-year survivability of breast cancer patients from a massive data set with imbalanced property. The proposed classifier algorithms are a combination of synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO), while integrating some well known classifiers, such as logistic regression, C5 decision tree (C5) model, and 1-nearest neighbor search. To justify the effectiveness for this new set of classifiers, the g-mean and accuracy indices are used as performance indexes; moreover, the proposed classifiers are compared with previous literatures. Experimental results show that the hybrid algorithm of SMOTE + PSO + C5 is the best one for 5-year survivability of breast cancer patient classification among all algorithm combinations. We conclude that, implementing SMOTE in appropriate searching algorithms such as PSO and classifiers such as C5 can significantly improve the effectiveness of classification for massive imbalanced data sets. |
| 關鍵字 | Breast cancer;Classification;Oversampling technique;Particle swarm optimization;Synthetic minority |
| 語言 | en |
| ISSN | 1568-4946 1872-9681 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | K.-J. Wang |
| 審稿制度 | 是 |
| 國別 | NLD |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107028 ) |